A scalable framework for spatiotemporal analysis of location-based social media data

Guofeng Cao, Shaowen Wang, Myunghwa Hwang, Anand Padmanabhan, Zhenhua Zhang, Kiumars Soltani

Research output: Contribution to journalArticle

57 Scopus citations

Abstract

In the past several years, social media (e.g., Twitter and Facebook) has experienced a spectacular rise in popularity and has become a ubiquitous location for discourse, content sharing and social networking. With the widespread adoption of mobile devices and location-based services, social media typically allows users to share the whereabouts of daily activities (e.g., check-ins and taking photos), thus strengthening the role of social media as a proxy for understanding human behaviors and complex social dynamics in geographic spaces. Unlike conventional spatiotemporal data, this new modality of data is dynamic, massive, and typically represented in a stream of unstructured media (e.g., texts and photos), which pose fundamental representation, modeling and computational challenges to conventional spatiotemporal analysis and geographic information science. In this paper, we describe a scalable computational framework to harness massive location-based social media data for efficient and systematic spatiotemporal data analysis. Within this framework, the concept of space-time trajectories (or paths) is applied to represent activity profiles of social media users. A hierarchical spatiotemporal data model, namely a spatiotemporal data cube model, is developed based on collections of space-time trajectories to represent the collective dynamics of social media users across aggregation boundaries at multiple spatiotemporal scales. The framework is implemented based upon a public data stream of Twitter feeds posted on the continent of North America. To demonstrate the advantages and performance of this framework, an interactive flow mapping interface (including both single-source and multiple-source flow mapping) is developed to allow real-time and interactive visual exploration of movement dynamics in massive location-based social media data at multiple scales.

Original languageEnglish
Pages (from-to)70-82
Number of pages13
JournalComputers, Environment and Urban Systems
Volume51
DOIs
StatePublished - May 1 2015

Keywords

  • Big data
  • CyberGIS
  • Data cube
  • OLAP
  • Social media

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